8000 FIX: change autolim exclude shapes not in data co-ordinates · matplotlib/matplotlib@41e75ec · GitHub
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FIX: change autolim exclude shapes not in data co-ordinates
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  • pre_transform_data.png
  • pre_transform_data.svg

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    ‎doc/api/next_api_changes/2019-03-04-AL.rst

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    Original file line numberDiff line numberDiff line change
    @@ -13,6 +13,41 @@ without an explicit call to `Axes.autoscale_view`.
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    In some cases, this can result in different limits being reported. If this is
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    an issue, consider triggering a draw with `fig.canvas.draw`.
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    Autoscaling changes for Collections
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    ```````````````````````````````````
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    Autoscaling has also changed for artists that are based on the `.Collection`
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    class. Previously, the method that calculates the automatic limits
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    `.Collection.get_datalim` tried to take into account the size of objects
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    in the collection and make the limits large enough to not clip any of the
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    object, i.e., for `.Axes.scatter` it would make the limits large enough to not
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    clip any markers in the scatter. This is problematic when the object size is
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    specified in physical space, or figure-relative space, because the transform
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    from physical units to data limits requires knowing the data limits, and
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    becomes invalid when the new limits are applied. This is an inverse
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    problem that is theoretically solvable (if the object is physically smaller
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    than the axes), but the extra complexity was not deemed worth it, particularly
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    as the most common use case is for markers in scatter that are usually small
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    enough to be accommodated by the default data limit margins.
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    While the new behavior is algorithmically simpler, it is conditional on
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    properties of the `.Collection` object:
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    1. ``offsets = None``, ``transform`` is a child of `.Axes.transData`: use the paths
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    for the automatic limits (i.e. for `.LineCollection` in `Axes.streamplot`).
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    2. ``offsets != None``, and ``offset_transform`` is child of `.Axes.transData`:
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    a) ``transform`` is child of `.Axes.transData`: use the ``path + offset`` for
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    limits (i.e., for `.Axes.bar`).
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    b) ``transform`` is not a child of `.Axes.transData`: just use the offsets
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    for the limits (i.e. for scatter)
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    3. otherwise return a null `.Bbox`.
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    While this seems complicated, the logic is simply to use the information from
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    the object that are in data space for the limits, but not information that is
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    in physical units.
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    LogLocator.nonsingular now maintains the orders of its arguments
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    ````````````````````````````````````````````````````````````````
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    ‎lib/matplotlib/collections.py

    Lines changed: 46 additions & 8 deletions
    Original file line numberDiff line numberDiff line change
    @@ -146,6 +146,8 @@ def __init__(self,
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    self._joinstyle = None
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    self._offsets = np.zeros((1, 2))
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    # save if offsets passed in were none...
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    self._offsetsNone = offsets is None
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    self._uniform_offsets = None
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    if offsets is not None:
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    offsets = np.asanyarray(offsets, float)
    @@ -179,9 +181,30 @@ def get_offset_transform(self):
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    return t
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    def get_datalim(self, transData):
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    # Get the automatic datalim of the collection.
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    #
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    # This operation depends on the transforms for the data in the
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    # collection and whether the collection has offsets.
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    #
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    # 1) offsets = None, transform child of transData: use the paths for
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    # the automatic limits (i.e. for LineCollection in streamline).
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    # 2) offsets != None: offset_transform is child of transData:
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    # a) transform is child of transData: use the path + offset for
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    # limits (i.e for bar).
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    # b) transform is not a child of transData: just use the offsets
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    # for the limits (i.e. for scatter)
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    # 3) otherwise return a null Bbox.
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    transform = self.get_transform()
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    transOffset = self.get_offset_transform()
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    if (not self._offsetsNone and
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    not transOffset.contains_branch(transData)):
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    # if there are offsets but in some co-ords other than data,
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    # then don't use them for autoscaling.
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    return transforms.Bbox.null()
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    offsets = self._offsets
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    paths = self.get_paths()
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    if not transform.is_affine:
    @@ -196,13 +219,29 @@ def get_datalim(self, transData):
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    # get_path_collection_extents handles nan but not masked arrays
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    if len(paths) and len(offsets):
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    result = mpath.get_path_collection_extents(
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    transform.frozen(), paths, self.get_transforms(),
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    offsets, transOffset.frozen())
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    result = result.inverse_transformed(transData)
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    else:
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    result = transforms.Bbox.null()
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    return result
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    if transform.contains_branch(transData):
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    # collections that are just in data units (like quiver)
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    # can properly have the axes limits set by their shape +
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    # offset. LineCollections that have no offsets can
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    # also use this algorithm (like streamplot).
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    result = mpath.get_path_collection_extents(
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    transform.frozen(), paths, self.get_transforms(),
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    offsets, transOffset.frozen())
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    return result.inverse_transformed(transData)
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    if not self._offsetsNone:
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    # this is for collections that have their paths (shapes)
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    # in physical, axes-relative, or figure-relative units
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    # (i.e. like scatter). We can't uniquely set limits based on
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    # those shapes, so we just set the limits based on their
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    # location.
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    # Finish the transform:
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    offsets = transOffset.transform(offsets)
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    offsets = transData.inverted().transform(offsets)
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    offsets = np.ma.masked_invalid(offsets)
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    if not offsets.mask.all():
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    points = np.row_stack((offsets.min(axis=0), offsets.max(axis=0)))
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    return transforms.Bbox(points)
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    return transforms.Bbox.null()
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    def get_window_extent(self, renderer):
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    # TODO: check to ensure that this does not fail for
    @@ -1299,7 +1338,6 @@ def __init__(self, segments, # Can be None.
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    antialiaseds = (mpl.rcParams['lines.antialiased'],)
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    colors = mcolors.to_rgba_array(colors)
    1302-
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    Collection.__init__(
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    self,
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    edgecolors=colors,

    ‎lib/matplotlib/tests/baseline_images/test_axes/scatter.pdf

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    ‎lib/matplotlib/tests/baseline_images/test_axes/scatter.png

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